--- title: AI Prediction & Demand Forecasting | Zentavor description: Demand and revenue forecasts that track reality, so you plan inventory and capacity with confidence. canonical: https://zentavor.com/solution-ai-prediction.html --- # AI Prediction & Demand Forecasting | Zentavor > Demand and revenue forecasts that track reality, so you plan inventory and capacity with confidence. Solutions / AI prediction Forecasts your planners can trust Demand and revenue forecasts that track reality closely, so you plan inventory, staffing and capacity with confidence Multi-horizonPer date-shop-SKURevenue-aware 9:41 Demand forecastper SKU · per storedaily → quarterly Retail / Supply chain · in production 95% On track MAPE 15–20% · date-shop-SKU SKU-4471 · cluster MSK-N · h+14d Forecast vs actual SKU-4471 · MSK-N842 u±4% SKU-2093 · SPB-11,210 u±6% multi-horizon84k series +13%revenue impact · in production −24%write-offs, fresh categories 250%ROI over 3 years What we build ## From noise to a forecast you can order against Gradient-boosted models per SKU learn cycles, promo elasticity and live shocks, and serve a forecast resolved at every planning horizon Raw demand stream · noisestreaming skushopdate7dqty ∑ Demand model gradient-boosted · per SKU Drivers & scenarios accounted for Sales history & trend 24 months Seasonality weekly + yearly cycles Promotions & price elasticity + events Weather & local events external feed Stockouts & cannibalization censored demand Fit on the historical period52% Forecast · SKU-4471 · Store cluster MSK-N DayWeekMonthQuarter min errormax profit actualsfitforecastconfidence Streaming raw demand. Thousands of date × shop × SKU events arrive as noise, no shape yet. MAPE · date-shop-SKU 0% forecast error band Out-of-stock −0% shelf availability up Revenue · $ +0% profit-aware targets ROI · 3 yrs 0% over baseline ops Two ways to miss: both expensive Every demand number has a two-sided cost. Forecast too low and you stock out: the sale walks to a competitor. Too high and cash freezes on the shelf, then gets marked down. Precision is what collapses both costs. Our forecast hugs real demand: the confidence band stays tight per date, shop and SKU. Multi-horizon planning ## One model, every planning cadence The same demand signal, resolved at four horizons and synced to your order cycles, so each team plans on a number built for their own decisions Day Store replenishment Per-store, per-SKU orders for tomorrow: the tightest band, refreshed daily. Week Staffing & promo prep Labour and promotion allocation ahead of the week's demand shape. Month Category & supplier Category plans and supplier orders aligned to real lead times. Quarter Procurement & capacity Volume commitments, warehousing and capacity for the season ahead. Per item, category and store, synced to your ordering cycles. The challenge ## Forecasts you can't order against Manual forecasting Manual forecasts miss seasonality, promos and shocks. Over-stock freezes cash; out-of-stock loses the sale. One global model ignores per-store, per-SKU behaviour. Accuracy metrics never connect to revenue. With Zentavor Gradient-boosted models per SKU learn cycles, promo elasticity and live shocks, hitting 15–20% MAPE. Profit-aware targets price both errors: −15–20% out-of-stock, −22–28% write-offs. A forecast resolved per date × shop × SKU: every store and item gets its own demand shape. Targets optimise revenue and margin, delivering +8–12% revenue recovery. FAQ ## Your frequently asked questions ** How accurate is the forecast?** We report error per horizon, not one global number: typically 15–20% MAPE at date × shop × SKU. Fresh and high-velocity categories are tracked separately because their cost of error is different. ** What data do you need to start?** About 24 months of sales history is enough to begin. We then add drivers incrementally: seasonality, promotions and price, weather and local events, and stockouts (censored demand). The fit sharpens with each. ** Does our data leave our environment?** No. The models run inside your perimeter, on-prem or private cloud, so demand and sales data never leave your environment. ** How long until we see the effect?** We pilot on your data and measure it with an A/B test on real revenue, read over weeks or months. This is the same way the +13% result was proven, not with a backtest. ** Does it work per-SKU at our scale?** Yes. Forecasts are produced per item, category and store, proven in production at 1,500 stores and 400,000 orders a day. ** Accuracy or revenue: what is optimized?** Revenue and margin, not just the error metric. Targets are revenue-aware, so the model spends its accuracy where it moves the business most. Let's talk ## Plan with forecasts you can trust Share your demand data and planning cycle. We'll scope a forecasting model and rollout Request a demo